Artificial Intelligence in Orthopaedics: The Future is Here
Syed S Ahmed*
Department of Trauma and Orthopaedics, Maidstone and Tunbridge Wells NHS Trust, United Kingdom
*Corresponding Author: Syed S Ahmed, Department of Trauma and Orthopaedics, Maidstone and Tunbridge Wells NHS Trust, United Kingdom.
Received:
May 27, 2024; Published: June 19, 2024
Abstract
Artificial Intelligence (AI) is an umbrella term theorising the replication of human intellect via computers [1]. It is the ability of machines to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. The term was initially coined in 1955 by a group of scientists, who proposed that these aforementioned aspects of human intellect could be simulated by machines. Since then, it has emerged as a revolutionary force across various domains of medicine and orthopaedics is no exception. Improvement in computer hardware, speeds, memory capacity and software has seen a surge in interest in the field of AI.
Keywords: Artificial Intelligence; Orthopaedics; Future
References
- Han XG and Tian W. “Artificial intelligence in orthopaedic surgery: Current state and future perspective”. Chinese Medical Journal (England) 21 (2021).
- Bernstein J. “Not the Last Word: ChatGPT Can’t Perform Orthopaedic Surgery”. Clinical Orthopaedics and Related Research® 4 (2023).
- Langerhuizen DWG., et al. “Is Deep Learning on Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?” Clinical Orthopaedics and Related Research®11 (2020).
- Liu P., et al. “Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era”. Frontiers in Bioengineering and Biotechnology 10 (2022):927926.
- Park CW., et al. “Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation”. PLoS One 17 (2022).
- Jang SJ., et al. “Comparison of tibial alignment parameters based on clinically relevant anatomical landmarks a deep learning radiological analysis”. Bone and Joint Open 10 (2022).
- Borjali A., et al. “Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network”. Journal of Orthopaedic Research7 (2020).
- Begum FA., et al. “Robotic technology: Current concepts, operative techniques and emerging uses in unicompartmental knee arthroplasty”. EFORT Open Reviews 5 (2020).
- Lisacek-Kiosoglous AB., et al. “Artificial intelligence in orthopaedic surgery EXPLORING ITS APPLICATIONS, LIMITATIONS, AND FUTURE DIRECTION Introduction: artificial intelligence, time for clear nomenclature”. Bone Joint Research7 (2023).
- Eweje FR., et al. “Deep Learning for Classification of Bone Lesions on Routine MRI”. EBioMedicine (2021): 68.
- Lindgren Belal S., et al. “Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases”. European Journal of Radiology (2019): 113.
- Loftus TJ., et al. “Artificial Intelligence and Surgical Decision-making”. JAMA Surgery2 (2020).
- Karnuta JM., et al. “The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty”. Journal of Shoulder and Elbow Surgery 11 (2020): 2385-2394.
- Borjali A., et al. “Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network”. Journal of Orthopaedic Research 7 (2020):1465-1471.
- Yi PH., et al. “Automated detection & classification of knee arthroplasty using deep learning”. Knee2 (2020).
- Stephen Hawking. “AI will be “either best or worst thing” for humanity | Stephen Hawking | The Guardian (2024).
Citation
Copyright